PortoSeguro
├── README.md # Current file.
├── doc
│ └── report.pdf # Final report.
├── load_data.py # Script to load data.
├── log # Training log of each model.
│ ├── gradient_boost.log
│ ├── logistic_regression.log
│ ├── neural_network.log
│ └── random_forest.log
├── notebook
│ ├── Plot-Hist-Corr.ipynb # Notebook for all plots in report.
│ └── dimension_reduction.ipynb # Attempted dimension reduction.
├── train_gb.py # Model of Gradient Boosting.
├── train_lr.py # Model of Logistic Regression.
├── train_nn.py # Model of Neural Network.
└── train_rf.py # Model of Random Forests.
The python scripts run on Python 3.6.3. The following libraries are necessary to run scripts or notebooks.
- matplotlib (2.1.0)
- numpy (1.13.3)
- pandas (0.20.3)
- scikit-image (0.13.0)
- scikit-learn (0.19.1)
- scipy (0.19.1)
- seaborn (0.8)
- tqdm (4.19.4)
The data file should be in ./data with filenames test.csv and train.csv.
Directly execute the script as follow:
python3 train_gb.py
python3 train_lr.py
python3 train_nn.py
python3 train_rf.py